SynCo jointly optimizes synthesizer and reasoner in the same online batch, reaching 78.72% average across eight math reasoning benchmarks
SynCo formulates agentic data synthesis and task solving as a coupled multi-agent reinforcement learning problem, updating a Synthesizer and a Reasoner from the same online interaction batch: the Reasoner receives rollout-level correctness rewards while the Synthesizer receives a task-level reward gated by task quality, answer reliability, and outcome-grounded teachability, with both updates feeding the next synthesis round. Across eight mathematical reasoning benchmarks, SynCo reaches 78.72% average accuracy versus 75.85% for a frozen-Synthesizer variant and 74.96% for the strongest external baseline, ScaleQuest-Math, with gains arising mainly from problems the initial model failed to solve.